Dominant Local Binary Patterns for Texture Classification

Transcription

IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 5, MAY 2009
1107
Dominant Local Binary Patterns
for Texture Classification
S. Liao, Max W. K. Law, and Albert C. S. Chung
Abstract—This paper proposes a novel approach to extract
image features for texture classification. The proposed features
are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local
binary patterns (DLBP) in a texture image and the supplementary
features extracted by using the circularly symmetric Gabor filter
responses. The dominant local binary pattern method makes use
of the most frequently occurred patterns to capture descriptive
textural information, while the Gabor-based features aim at
supplying additional global textural information to the DLBP
features. Through experiments, the proposed approach has been
intensively evaluated by applying a large number of classification
tests to histogram-equalized, randomly rotated and noise corrupted images in Outex, Brodatz, Meastex, and CUReT texture
image databases. Our method has also been compared with six
published texture features in the experiments. It is experimentally
demonstrated that the proposed method achieves the highest
classification accuracy in various texture databases and image
conditions.
Index Terms—Circularly symmetric Gabor filter, local binary
pattern, rotation invariance, texture classification.
I. INTRODUCTION
EXTURE classification plays an important role in computer vision and image processing applications. The applications include medical image analysis and understanding,
remote sensing, object-based image coding, and image retrieval.
As the demand of such applications increases, texture classification has received considerable attention over the last several
decades and numerous novel methods have been proposed.
For example, Chellappa et al. used the Gaussian Markov
random fields (GMRF) to model texture patterns based on
statistical relationship between adjacent pixel intensity values
[4]. Bovik et al. applied the Gabor filters to an image and then
computed the average filter responses as features [2]. Mallat
introduced the multiresolution wavelet decomposition method,
which generates coefficients in the HL, LH, and LL channels
for subsequent classification tasks [18]. Weszka et al. [23]
applied the co-occurrence matrix to extract the mean intensity,
contrast, and correlation information from the texture images.
T
Manuscript received December 07, 2007; revised December 11, 2008. Current version published April 10, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Dan Schonfeld.
The authors are with the Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Digital Object Identifier 10.1109/TIP.2009.2015682
However, the above techniques only encode the absolute texture orientation information, which is inadequate for rotation
invariant texture classification.
Therefore, to achieve rotation invariance in texture classification, researchers have attempted to either discard all orientation information or capture relative orientation information. To
discard orientation information, Porter and Canagarajah [20] removed the HH wavelet channels and combined the LH and HL
wavelet channels to obtain rotation invariant wavelet features.
Haley and Manjunath [8] calculated isotropic rotation invariant
features from Gabor filters. Kashyap and Khotanzad [12] constructed an isotropic circular Gaussian Markov random field
(ICGMRF). On the other hand, some approaches capture the
relative directional features rather than the absolute orientation
information. Deng and Clausi [6] extended the ICGMRF model
[12] into anisotropic circular GMRF model (ACGMRF) to capture rotation invariant relative orientation features. Along the
same research line, Arof and Deravi [1] utilized similar circular
neighborhoods with 1-D DFT transformation.
Although the aforementioned methods are proofed to be rotation invariant, they are sensitive to the change of illumination condition which often exists in texture images because of
the limitation of the imaging devices or the change of lighting
condition. In real world applications, histogram equalization is
often performed to mitigate the adverse effect of varying illumination condition. However, as we will show in the Experiments and Results Section (Section IV), the performances of the
above methods drop significantly after the histogram equalization has been applied because, for these methods, intensity mean
and image contrast are two important pieces of textural information for texture classification. Ojala et al. [19] proposed rotation and histogram equalization invariant features by observing
the statistical distributions of the uniform local binary patterns
(LBPs). Huang et al. extended the LBP method by calculating
the derivative-based LBPs in the application of face alignment
[9]. However, the uniform LBPs are not the dominating patterns
(i.e., patterns of the largest proportions in an image) in some textures with irregular edges and shapes. This observation will be
further elaborated in Section II.
In this paper, we are motivated to propose a new feature extraction method that is robust to histogram equalization and rotation. First, the conventional LBP approach is extended to the
dominant local binary pattern (DLBP) approach in order to effectively capture the dominating patterns in texture images. Unlike the conventional LBP approach, which only exploits the
uniform LBP, given a texture image, the DLBP approach computes the occurrence frequencies of all rotation invariant patterns defined in the LBP groups. These patterns are then sorted
in descending order. The first several most frequently occurring
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patterns should contain dominating patterns in the image and,
therefore, are the dominant patterns. It is shown that the DLBP
approach is more reliable to represent the dominating pattern
information in the texture images.
Although the DLBP features encapsulate more textural information than the conventional LBP features, they lack the consideration of distant pixel interactions. The reason is that the
binary patterns are extracted in the proximity of local pixels.
The pixel interaction that takes place outside the local neighborhood system is unconsidered in LBP or DLBP. To replenish
the missing information in the DLBP features, an additional feature set, features based on the Gabor filter responses are utilized
as the supplement to the DLBP features. The Gabor-based features are computed from the normalized average magnitudes of
circularly symmetric Gabor filter responses. They are rotation
invariant and also less sensitive to histogram equalization. The
Gabor-based features are well complemented with the DLBP
ones. It is experimentally shown that the fused image features
yield significant higher texture classification rates than the mere
use of either the DLBP features or the Gabor-based features.
This paper is organized as follows. Section II describes the
feature extraction procedure using the dominant local binary
patterns (DLBP). Section III then gives the feature extraction
procedure using Gabor filters. Section IV presents the experimental results as well as the details of the experiments, including
the methods in the comparison study, a brief description of the
support vector machines (SVM) classifier, and the experiment
setups. Section V gives the conclusions of this paper.
Fig. 1. Example of finding the binary labels of eight circular neighboring pixels
at locations t ; t ; . . ., and t , given the center pixel t . u(t
t ) represents
a step function, where u(x) = 1 when x 0; else, u(x) = 0.
0
II. DOMINANT LOCAL BINARY PATTERNS (DLBP)
A. An Overview of Local Binary Patterns (LBP)
The method using local binary patterns (LBPs) is first proposed by Ojala et al. [19] to encode the pixel-wise information
in the texture images. At a center pixel , each neighboring
pixel is assigned with a binary label, which can be either “0”
or “1,” depending on whether the center pixel has higher intensity value than the neighboring pixel (see Fig. 1 for an illustration). The neighboring pixels are the angularly evenly distributed sample points over a circle with radius centered at
the center pixel. The LBP label for that center pixel is given by
(1)
where represents the center pixel, is the th neighboring
pixel,
, is the total number of neighboring
pixels, is the circle radius which determines how far the neighboring pixels are located away from the center pixel, and
if
else
. The value of is assigned according
to the value of as suggested in [19]. In our implementation,
when
;
when
; and
when
. It is noted that computing LBP based on (1) is a rotation invariant operation. Rotating an image causes the circular
, and
.
shifting of the binary labels at locations
This shifting effect can be eliminated by finding the minimum
value among all possible values of in (1). This minimum value
denotes the rotation invariant LBP at the center pixel . Furthermore, the absolute pixel intensity information at and
Fig. 2. Proportions of the uniform LBPs for R = 2 and R = 3 in the texture
images obtained from the Brodatz database (a), (b), Meastex database (c), (d)
and CURet database (e), (f).
is discarded by using the step function
in (1) when
calculating LBP. Therefore, the LBP operator is not sensitive to
histogram equalization.
B. Dominant Local Binary Patterns (DLBP)
In the conventional LBP method proposed by Ojala et
al. [19], only the uniform LBPs are considered. At a pixel,
it gives a uniform LBP if the corresponding binary label
sequence has no more than two transitions between “0” and
“1” among all pairs of the adjacent binary labels. For example,
the binary label sequences “10001111” and “00011000” are
uniform LBPs. But the sequence “01001111” is not a uniform
LBP because it has four transitions. In the textures which
mostly consist of straight edges or low curvature edges, the
uniform LBPs effectively capture the fundamental information
of textures. However, in practice, there are some texture images
LIAO et al.: DOMINANT LOCAL BINARY PATTERNS FOR TEXTURE CLASSIFICATION
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Fig. 3. Proportions of the first x patterns among all patterns appeared in the texture images of the (a) Brodatz database, (b) Meastex database, and (c) CUReT
database.
having more complicated shapes. These shapes can contain high
curvature edges, crossing boundaries or corners. Performing
texture classification on these textures based on uniform LBPs
is possibly problematic. The reason is that the uniform LBPs
extracted from such images are not necessary to be the patterns
having dominating proportions. For illustration, Fig. 2 shows
the proportions of the uniform LBPs extracted from texture
images of the Brodatz, Meastex and CURet databases. It is
observed that the uniform LBPs are not necessary to occupy the
major pattern proportions. Consequently, textural information
cannot be effectively represented by solely considering the
histogram of the uniform LBPs.
Although utilizing the uniform LBPs is insufficient to
capture textural information, we avoid considering all the
possible patterns to perform classification. As pointed out by
Ojala et al. [19], the occurrence frequencies of different patterns
vary greatly and some of the patterns rarely occur in a texture
image. The proportions of these patterns are too small and
inadequate to provide a reliable estimation of the occurrence
possibilities of these patterns.
Therefore, we propose to use dominant local binary patterns
(DLBPs) which consider the most frequently occurred patterns
in a texture image. It avoids the aforementioned problems encountered by merely using the uniform LBPs or making use
of all the possible patterns, as the DLBPs are defined to be
the most frequently occurred patterns. In this paper, it will be
demonstrated that a minimum set of pattern labels that represents around 80% of the total pattern occurrences in an image
can effectively captures the image textural information for classification tasks. Figs. 3(a)–(c) shows the pattern proportions occupied by different numbers of most frequently occurred patterns in the texture images obtained from the Brodatz, Meastex,
and CUReT databases, respectively.
In practice, given a set of training images, the required
number of patterns to occupy 80% pattern occurrences is
determined prior to extracting the features of DLBP. This
required number of patterns remains the same as the DLBP
features are subsequently extracted from the training image set
or new testing images. Nonetheless, for two different texture
images, the dominant patterns can be of different types. That
is, the DLBP approach is not limited to consider only a fixed
set of patterns (e.g., uniform patterns). This is distinct to the
conventional LBP framework, in which the final feature vector
representing an input image is the occurrence histogram of the
fixed set of uniform patterns.
To retrieve the DLBP feature vectors from an input image, the
pattern histogram which considers all the patterns in the input
image is constructed and the histogram bins are sorted in nonincreasing order. Based on the previously computed number of
patterns, the occurrence frequencies corresponding to the most
frequently occurred patterns in the input image are served as
the feature vectors. It is noted that the DLBP feature vectors do
not bear information regarding the dominant pattern types, and
they only contain the information about the pattern occurrence
frequencies. According to the experimental results, omitting the
dominant pattern type information in the DLBP feature vectors
is not harmful. It is because the 80% dominant patterns of DLBP
is a preponderance of the overall patterns, and the DLBP feature
vectors are already very descriptive. It is practically improbable
to have two distinct texture types which can resemble dominant
pattern proportions of each other.
Without encapsulating the pattern type information, the
DLBP features also possess surpassing robustness against
image noise, as compared to the conventional LBP features.
Under the effect of image noise, the binary label of a neighboring pixel is possible to be flipped by the intensity distortion
induced by noise. Flipped binary labels alter the extracted
LBPs. As a result, even though some LBPs are computed on
the same type of image structures, the extracted LBP type can
vary significantly. Thus, the pattern type information is unreliable. In the conventional LBP framework, the pattern types
are categorized as uniform patterns or nonuniform patterns. In
which, under the effect of image noise, a large amount of useful
patterns turns into nonuniform ones that are unconsidered in the
conventional LBP method. On the contrary, the DLBP approach
processes all 80% dominant patterns disregarding the pattern
types. As reflected by the noisy texture classification experiments in Section IV-D, the number of dominant patterns for
classifying noisy textures soars as the noise level increases [see
the rows of “Experiment setup #2 (noisy textures)” in Table I].
The DLBP approach is then capable of capturing more pattern
types to deal with the pattern distortion induced by image noise.
It in turn retains the high DLBP-based classification accuracies
of noise corrupted textures.
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TABLE I
NUMBER OF DOMINANT PATTERNS OF
(
) IN THE EXPERIMENT SETUP #1 AND #2. IN THE ROW OF “EXPERIMENT SETUP #2 (NOISY
TEXTURES),” THE AVERAGE NUMBERS OF DOMINANT PATTERNS OBTAINED IN SIX INDEPENDENT NOISY TESTS ARE SHOWN
DLBP
DLBP
As a summary, the pseudo codes on determining the number
of dominant patterns of DLBP and extracting DLBP feature vectors are presented in Algorithm 1 and Algorithm 2, respectively.
The computational complexity of DLBP is analyzed as follows.
Assuming that neighboring pixels are considered with respect
to each center pixel for the evaluation of the (1), and there
are
pixels in total for an input image. The time complexity
because circular shiftings are reto evaluate (1) is
pixels in total. For the
quired for each pixel, and there are
elements
sake of implementation simplicity, an array with
is utilized to represent the pattern histogram (i.e., the length of
the occurrence histogram in Algorithms 1 and 2). Therefore,
the sorting of the histogram takes
time. The time complexity of DLBP is
.
Since the value of is a user specified parameter (
or
24 in our experiments), it can be regarded as a constant. Thus,
the computation of DLBP is a linear time process.
10.
.
11. END FOR
12.
13. Return
Algorithm 2 Extracting a DLBP feature vector
Input: A training or a testing image , the required number
of dominant patterns
, and the parameters and for
DLBP
Output: The DLBP feature vector corresponding to image
Algorithm 1 Determining the number of dominant patterns
of DLBP
1. Initialize the pattern histogram,
Input: Training image set, and the parameters
DLBP
3.
Compute the pattern label of
4.
Increase the corresponding bin by 1,
Output: The required number of patterns
occurrences
1. Initialize
and
for
for 80% pattern
(1)
5. END FOR
7. Return
in the training image set
3.
Initialize the pattern histogram,
4.
FOR each center pixel
as the feature vector of DLBP
III. SUPPLEMENT TO DOMINANT LOCAL BINARY PATTERNS
5.
Compute the pattern label of
6.
Increase the corresponding bin by 1,
,
7.
END FOR
8.
Sort the histogram in descending order
9. Find the number of patterns
in
,
6. Sort the histogram in descending order
.
2. FOR each image
2. FOR each center pixel
(1)
for 80% pattern occurrences
DLBP is capable of encoding the pixel-wise information in
the texture images. However, it does not take into account the
long range pixel interaction that takes place outside the coverage
of its circular neighborhood system, which normally has a radius
of 2 or 3 pixels. It is noted that capturing such interaction is vital
to provide descriptive features for texture classification. In this
section, we elaborate the procedure to extract global features
from the Gabor filter responses to complement with the DLBP
features.
A. Overview of Circularly Symmetric Gabor Filters
Computing an average response magnitude from a Gabor
filtered image is a widely used feature extraction method [2],
[8], [11], [15], [16], [21], [24], [27]. Since Gabor filters can be
viewed as bandpass filters, the average magnitude response for
each Gabor filtered image can reflect the image signal power
of the corresponding filter passing band. The passing band
of a traditional Gabor filter is orientation dependent, which
makes it orientation sensitive and rotation variant. To achieve
rotation invariance, circularly symmetric Gabor filters are used
because each filter has the same passing band in all directions.
Therefore, it is capable of extracting information regarding the
image signal strength associated with the filter passing band,
disregarding the orientation information.
A circularly symmetric Gabor filter is constructed in the
Fourier domain and its formulation in the Fourier domain is
given by
(2)
where and are frequencies in and directions, respectively, controls the passing bandwidth of the filter and is
the center frequency of the filter passing band. With regard to
the passing bands of the Gabor filters being utilized, the design
strategy of the Gabor filters, presented in [26], is employed. This
strategy maximizes the coverage of the frequency domain while
minimizing the overlap between filters. In this paper, we use
,
,
and
four circularly symmetric Gabor filters,
with different corresponding center frequencies (measured in
,
,
and
.
cycles/image)
The values of are different for these four filters,
,
,
, and
so that the upper
,
and
are the same as the lower
cut-off frequencies of
and
in order to cover sufficient
cut-off frequencies of ,
bandwidths in the Fourier domain to perform classification, the
design strategy can be referred to [20].
B. Utilizing Gabor Filter Responses
Conventionally, the averages of the Gabor filter response
magnitudes are exploited to represent a Gabor filtered texture
image. The average magnitude reflects the signal strength of an
image in a particular frequency band. A vital drawback of using
the average magnitudes is that it is very sensitive to histogram
equalization. This drawback conflicts to the DLBP method,
which is invariant to histogram equalization.
To overcome the aforementioned drawback, the Gabor average magnitudes are normalized prior to being utilized as tex,
,
and
are the averaged
tural features. Suppose
,
,
magnitudes of the image filtered by the Gabor filters
and
, respectively, and
, the
features extracted from the Gabor filter responses are
,
,
and
.
The above normalized average magnitudes quantify the distribution of the image signal power falling in various passing
bands of the corresponding Gabor filters. The image DC signal
is out of the passing bands of all the Gabor filters being used.
Therefore, the normalized average Gabor filter response magnitudes are invariant to the change of the image intensity average
caused by histogram equalization. Meanwhile, the image contrast information is eliminated in the normalized average mag-
1111
nitudes which is now invariant to the change of intensity variance [13]. The contrast information is unreliable under the effect
of histogram equalization. The normalized average Gabor filter
response magnitudes are, therefore, less sensitive to histogram
equalization. Thus, it is suitable to provide global information
as a supplement to the histogram equalization insensitive DLBP
features, which lacks the consideration of distant pixel interactions.
IV. EXPERIMENTS AND RESULTS
The proposed methods are evaluated against six published
approaches under various image conditions. Prior to the discussion of experimental results, we elaborate the implementation details of the experiments. Meanwhile, two sets of experiments have been carried out for testing. The first experiment setup presented in Section IV-C aims at demonstrating
that our experimental implementation can replicate the classification results as reported in the state-of-the-art study [19]. This
setup utilizes the same texture databases (Brodatz and Outex
databases), which were used in the experiments for the evaluation of the LBP approach, see [19] for details of the experiments and databases. The second experiment setup thoroughly
and intensively examines the classification performance of various approaches under five different conditions, original textures, randomly rotated textures, histogram-equalized textures,
histogram-equalized and randomly rotated textures, and noise
corrupted textures.
A. Methods in the Comparison Study
The classification accuracies of the proposed features have
been compared with six well founded feature extraction techniques for texture classification in this section. These six published techniques include.
• Daubechies wavelet transform features (DBWP) [18],
[20]: The input texture image is decomposed into three
levels. The HH channel of each level is discarded in the
experiments because it mainly reflects the noise property
in the images. The feature vector has seven dimensions,
which consist of the averaged
norms of the coefficients
in the HL and LH channels at all three levels, plus the
norm of the coefficients in the LL channel.
averaged
• Rotation invariant Daubechies wavelet transform features
(RDBWP) [20]: This is the rotation invariant version of
DBWP described above. The input image is decomposed
into three levels. The rotation invariance is achieved by
norms
taking an average of the corresponding averaged
of the coefficients in the HL and LH channels at each level.
As such, the feature vector has four dimensions.
• Traditional Gabor filters (TGF) [2]: Eight Gabor filters are
2.00, 3.17, 5.04
employed with center frequencies,
and 8.00 and oriented at angles of 0 and 90 degrees to
achieve optimal coverage in the Fourier domain. The averaged response magnitude of each filtered image is used
as one of the features. The feature vector has eight dimensions.
• Circular Gabor filters (CGF) [8]: Four circularly sym2.00,
metric Gabor filter with center frequencies
3.17, 5.04 and 8.00 are employed. The averaged response
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magnitude of each filtered image is utilized as one of the
features. The feature vector has four dimensions.
• Anisotropic circular Gaussian MRFs (ACGMRF) [6]: It
is an improved version of the Gaussian Markov random
field method [4]. It is rotation invariant and sensitive to
directional features. In total, 19 parameters are calculated
using the approximated least square estimation based on
a third-order neighborhood system with 24 symmetrical
orientations.
• Uniform local binary patterns (LBP) [19]: The occurrence
histograms of the uniform local binary patterns are com8, 16, 24 with
1, 2, 3, respectively.
puted using
The features are obtained by combining the three sets of
,
;
,
;
features computed over
,
together. It is claimed to have the best
and
performance of the local binary patterns in the experiments
conducted by Ojala et al. [19]. Noted that the local variance measure suggested in [19] is not employed along with
the LBP features as this measure is sensitive to the change
of illumination conditions of textures [19]. The number of
; 18 from
feature dimensions is 54 in LBP: 10 from
; and 26 from
.
,
,
• The proposed methods (NGF,
, and
): The classification performance of the above techniques are studied
along with the proposed methods, which embody two components—the DLBP features and the normalized average
magnitudes of circularly symmetric Gabor filter responses.
For the DLBP approach, we also study the classification
and
in (1). With the supplement
rate by using
of Gabor features, there are in total five different types of
features based on the proposed methods including normalized Gabor filter response average magnitudes (NGF), the
and
DLBP features with different values of (
) and their fused features (
and
). It is worth mentioning that
the number of image features extracted by the DLBP approach varies as training images change. The lengths of the
DLBP feature vectors for different testing cases are listed
in Table I.
B. Implementation Details of the Experiments
Analogous to the works presented in [10] and [14], each feature extraction technique is evaluated and compared with others
by passing the features through the same classifier. To appropriately examine the classification performance of various techniques, the support vector machines (SVM) [25], which is well
developed to handle classification problems, is employed in this
paper as the classifier to perform texture classification based
on the extracted features. Based on two-class SVMs, a multiclass classification scheme is constructed by cascading multiple
standard two-class SVM classifiers [25] to classify textures with
multiple classes.
Given that a texture classification problem involving
kinds of textures, each two-class SVM classifier is trained by
making use of two distinct types of texture images. There are in
two-class SVM classifiers. Each two-class
total
SVM classifier finds the optimal hyperplane to partition the
feature space into two halves in order to distinguish the training
samples belonging to the two given classes.
is the feature vector of the th training sample,
Suppose
which has a class label . The values of the class labels are
. We define a function
represented as indices
, which maps each class label to a binary value
if
if
(3)
and its inverse
, i.e.,
. Each SVM classifier
belongs to either
predicts whether a new testing sample
. The class prediction
class or class , where
is given as
(4)
where
represents the bias parameter of the optimal hyperplane of the SVM dedicated to classify the texture classes and
,
are non-negative Lagrangian multipliers for the training
sample used in the SVM associated with the texture classes
and . The values of
and
are estimated by maximizing
the margin of the decision boundary to classify feature samples
belonging to class and samples belonging to class . A predefined constant is employed to deal with the cases that training
samples cannot be well separated by the decision boundary. This
parameter is the upper bound of the Lagrangian multipliers, i.e.,
. Meanwhile, is a radial basis kernel function,
, where
is the Euclidean
-Norm and is a parameter governing the spread of .
Ideally, the SVM classifiers associated with the true class
label of a testing sample deliver correct classification results,
while the rest of the classifiers randomly report wrong class labels to that sample. As such, the final multiclass classification
result can be retrieved by finding the class label, which is mostly
two-class SVM classifiers.
reported by all the
The experiments are conducted in MatLab version 7.2. A publicly available MatLab SVM Toolbox [17] is employed. The feature vectors are normalized to have zero mean and unit variance
along each dimension. The values of the parameters and of
SVMs are specified using a grid search scheme. In this scheme,
the parameters and are searched exponentially in the ranges
and
, respectively, with a step size of
of
to probe the highest classification rate. The bilinear interpolation method is applied to retrieve the intensity at the positions
that are not on the image grid.
C. Experiment Setup #1
In the first experiment setup, we utilized the same set of texture images, which was also used in the performance study reported in [19]. The main reason to carry out this experiment is
to demonstrate that our experiment implementation is capable
of producing similar testing results as presented in [19].
1) Texture Databases: Brodatz (16 Classes): The sixteen
textures in the Brodatz database were downloaded from the
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TABLE II
PERFORMANCE OF LBP AND THE PROPOSED METHODS IN THE Brodatz DATABASE. THE CLASSIFICATION ACCURACY OF LBP PUBLISHED IN [19] IS BRACKETED
IN THE FIRST COLUMN
TABLE III
PERFORMANCE OF LBP AND THE PROPOSED METHODS IN THE Outex DATABASE. THE CLASSIFICATION ACCURACIES OF LBP PUBLISHED IN [19] ARE
BRACKETED IN THE THIRD COLUMN
Fig. 4. Texture images with the illumination condition “inca” and zero rotation
angle from the 24 classes of textures on Outex database.
website1 of the authors of [19]. These sixteen Brodatz textures
were previously utilized to conduct the texture classification
experiments presented in [19]. There were eight samples for
each texture class and the first sample was utilized for training.
The rest of the images were served as the testing samples. The
training samples were then artificially rotated in four different
angles 0 , 30 , 45 and 60 , while the testing set consisted
of rotation angles 20 , 70 , 90 , 120 , 135 , and 150 . As
such, there were in total four training images and 42 images
presented in the testing set for each texture class. Finally, each
image was split into 121 disjoint 16 16 subimages for both
training and testing in the experiments.
Outex: The Outex database contains textural images captured
from a wide variety of real materials. The texture images in the
Outex database are presented as test suites. The test suites comprise of different textures and are used for evaluating algorithms
for various types of texture analysis. In the experiment setup
#1, we made use of the Outex test suites Outex_TC_0010 and
Outex_TC_0012, which were created for the classification of
rotation invariant textures, and also for the classification of rotation and illumination invariant textures, respectively. In these
two test suites, there are 24 classes of texture images captured
under various illumination conditions, referred as “inca” (see
Fig. 4), “tl84” and “horizon.” For each condition, there are nine
rotation angles, 0 , 5 , 10 , 15 , 30 , 45 , 60 , 75 , and 90 . In
a given illumination condition, each rotation angle consists of
twenty 128 128 images for each texture class. The 24 20
samples with illumination condition “inca” and rotation angle
0 were adopted as the training data. Three tests using three individual testing sets, which comprised of Outex textures acquired
1http://www.ee.oulu.fi/mvg/page/image_data.
under the illumination conditions “inca,” “tl84,” and “horizon,”
were conducted.
2) Classification Accuracy: Tables II (first column) and III
(third column) list the classification accuracies of LBP. These
accuracies are obtained based on the experiment setup #1, which
follows closely the testing environments presented in [19]. As
a comparison, according to [19], the classification accuracies of
LBP are listed in the brackets of the same columns. In these tables, by comparing the values of LBP with the corresponding
bracketed values, it is observed that our experiment implementation is able to obtain similar testing results of LBP as compared
to those results presented in [19]. The classification accuracies
of the proposed methods are also listed in the tables. It is experimentally shown that our methods using both DLBP and Gabor
filter responses can achieve high accuracy, as compared with
LBP.
D. Experiment Setup #2
In this section, the performance of the proposed features
has been evaluated on three databases with large sets of image
textures. The three databases are Brodatz album [3], Meastex
database [22], and CUReT database [5]. For these databases,
classification accuracies have been measured in five different
subsets of experiments using original textures (subset #1),
histogram-equalized textures (subset #2), randomly-rotated
textures (subset #3), histogram-equalized and randomly-rotated textures (subset #4), and textures corrupted by additive
Gaussian noise (subset #5).
The reasons of performing the above sets of experiments and
their details are the followings. First, applying histogram equalization on texture images aims at minimizing the differences in
image contrast and illumination condition among different texture samples. However, histogram equalization also alters the
image gray level properties of the texture samples, in particular,
the mean and standard deviation. As will be shown in the experiment subsets #2 and #4, this can affect the texture classification accuracy of the features, which heavily depend on image
intensity mean and standard deviation. In the experiment subsets #2 and #4, all texture images, including training and testing
images, were histogram-equalized by using the same transformation function, , which is given as [7],
1114
Fig. 5. 24 texture images from the Brodatz album.
, where
and
represent the output and input
image intensity levels,
, is the number of
in this paper),
is the number of
intensity levels (
pixels having intensity level , and denotes the total number
of pixels in an image.
Second, randomly rotating the texture samples can help validate the rotation invariance of different features. In the experiment subsets #3 and #4, for each database, each texture sample
was rotated according to a randomly generated angle. The angle
fell uniformly within 0 and 360 degrees. The classification tests
for different features were performed on each database and repeated ten times. Then, the mean accuracy and its associated
standard deviation were estimated for the experiment subsets
#3 and #4 using the randomly-rotated textures.
Finally, performing texture classification under a noisy environment can examine the robustness against noise in real world
applications of different features. In experiment subset #5, for
each database, each texture sample was corrupted by the zero
mean additive Gaussian noise, where its standard deviation
was determined according to the corresponding SNR value.
The classification tests were conducted on each database and
repeated eight times. For each database, the mean and standard
deviation of the classification accuracy were obtained for the
comparison between different features.
1) Texture Databases: Brodatz (24 Classes): There are
twenty four homogeneous texture images selected from the
Brodatz album [3] (see Fig. 5). Each texture image has the
size of 640 640 pixels and represents a texture class. In the
experiments, each texture image was partitioned into twenty
128
five nonoverlapping sub-images with the size of 128
pixels. For the experiments using the original textures and
histogram equalized textures, each 128 128 sub-image was
64 pixels by taking the
downsampled to the size of 64
average between four adjacent pixels. To avoid the boundary
problem when the sub-images were rotated, for each 128 128
sub-image, instead of downsampling, only the center 64 64
pixels were used in the experiments. Therefore, in all the experiments, twenty five 64 64 texture samples were generated
for each texture class. The SVM classifier was trained by using
thirteen samples of each class. The other twelve samples were
used for validation.
Meastex: In the Meastex database [22], there are sixty nine
texture images selected (see Fig. 6). Each image has the size of
512 512 pixels. The texture images are categorized into 28
kinds of homogeneous textures. For the experiments using the
original textures and histogram equalized textures, each 512
512 texture image was partitioned into sixty four nonoverlapping sub-images with the size of 64 64 pixels. For the experiments using the rotated textures, each texture image was parti-
Fig. 6. 69 texture images from the Meastex database.
Fig. 7. 47 texture images from the CUReT database.
tioned into sixteen 128 128 nonoverlapping sub-images. The
center 64 64 pixels were then used. As such, sixteen samples
were generated for each texture image for the experiments using
rotated textures. In all the experiments, half of the samples were
placed in the training set, while the rest of the samples were put
in the test-sets.
CUReT: For the CUReT database [5], there are forty seven
kinds of homogeneous textures (see Fig. 7). Each kind of textures is represented by one texture image, which has the size of
320 320 pixels. We partitioned each 320 320 texture image
106 nonoverlapping sub-images. For the exinto nine 106
periments using the original textures and histogram equalized
textures, the center 64 64 pixels were used without the need
of interpolation. For the rotated texture-based experiments, the
center 64 64 pixels were used after rotation. In total, nine 64
64 samples were generated for each texture image. Five samples were used as the training data, and the other four samples
were utilized as the testing data.
E. Classification Accuracy
The experimental results on classification accuracy are presented in Tables IV–IX. The proposed methods are compared
with other six widely used methods, which include DBWP
(Row 1), RDBWP (Row 2), TGF (Row 3), CGF (Row 4),
ACGMRF (Row 5), and LBP (Row 6).
For the results obtained using NGF alone, see Row 7 in the
tables. For the results obtained using DLBP alone with
and
, see
(Row 8) and
(Row
9), respectively. For the results obtained using both the DLBP
1115
TABLE IV
PERFORMANCE OF DIFFERENT FEATURES OF 64 64 IMAGE RESOLUTIONS IN THE Brodatz Database. FOR EACH TEST (EACH COLUMN CORRESPONDINGLY),
THE HIGHEST TWO MEAN CLASSIFICATION ACCURACIES ARE HIGHLIGHTED IN BOLD
2
TABLE V
PERFORMANCE OF DIFFERENT FEATURES OF 64 64 IMAGE RESOLUTION IN THE Meastex Database. FOR EACH TEST (EACH COLUMN CORRESPONDINGLY), THE
HIGHEST TWO MEAN CLASSIFICATION ACCURACIES ARE HIGHLIGHTED IN BOLD
2
and the Gabor features, see
(Row 10) and
(Row 11), respectively.
1) Results on the Original Textures: In the second column of
Tables IV–VI, the use of dominant patterns instead of uniform
patterns is justified by comparing the classification accuracies
in the 8th and 9th rows with the sixth row. DLBP methods with
and
give higher accuracies than the conventional
LBP. Furthermore, the improvement of using DLBP instead of
conventional LBP for the texture databases Meastex and CUReT
(Tables V and VI, respectively) is higher than those for Brodatz
(Table IV). It is because the texture images in the Brodatz database contain more obvious edges, and, thus, it has higher proportion of uniform patterns than those in the images of Meastex
and CUReT.
2) Rotation Invariance: Experiment subsets #3 and #4 have
been carried out to study the rotation invariance property of our
features and other features. The experimental results are listed
in Table IV for the Brodatz album, Table V for the MeasTex
database, and Table VI for the CUReT database. In each table,
to study the performance regarding the rotation invariance, we
can compare the classification accuracies of different features
between the “Original Textures” (2nd column) and the “Randomly-rotated Textures” (fourth column). The accuracies can
also be compared between the “Histogram-equalized Textures”
(third column) and the “Histogram-equalized & Randomly-rotated Textures” (fifth column). In the tables, the results of other
features and our methods are listed in Rows 1–6 and Rows 7–11,
respectively.
From the tables, it is observed that our methods (Rows 7–11)
do not give significant accuracy drop when image rotation is
applied, as compared with other features, e.g., DBWP, TGF and
GMRF. In the experiments, the performance of different values
of the radius parameter of DLBP is tested when is equal to
case is not considered in the experiments,
2 and 3. The
the neighborhoods
as pointed out by [19] that when
are limited to the short distance referring to the center pixel,
and, therefore, the pattern’s structural property may not be well
reflected. Under the same image resolution and image condition,
and
) outperform
the DLBP features (for both
the other six methods in the comparison. As we can see, when
, fusing the DLBP features with the Gabor-based features
.
yields higher classification accuracy than the case when
3) Less Sensitive to Histogram Equalization: In Table IV, we
can compare the accuracies 1) between the “Original Textures”
(second column) and “Histogram-equalized Textures” (third
column); and 2) between the “Randomly-rotated Textures”
(fourth column) and “Histogram-equalized & Randomly-rotated Textures” (fifth column). It is found that our methods also
1116
TABLE VI
PERFORMANCE OF DIFFERENT FEATURES OF 64 64 IMAGE RESOLUTION IN THE CUReT Database. FOR EACH TEST (EACH COLUMN CORRESPONDINGLY), THE
HIGHEST TWO MEAN CLASSIFICATION ACCURACIES ARE HIGHLIGHTED IN BOLD
2
TABLE VII
PERFORMANCE OF DIFFERENT FEATURES OF 64 64 IMAGE RESOLUTION IN THE Brodatz Database IN THE ADDITIVE GAUSSIAN NOISE ENVIRONMENT OF
DIFFERENT SIGNAL-TO-NOISE RATIOS (SNR). FOR EACH TEST (EACH COLUMN CORRESPONDINGLY), THE HIGHEST TWO MEAN CLASSIFICATION ACCURACIES
ARE HIGHLIGHTED IN BOLD
2
do not have significant accuracy drop. LBP, DLBP and NGF
are robust to histogram equalization. Obviously, the uniform
binary patterns and nonuniform binary patterns of LBP and
DLBP are not affected by histogram equalization, which is
an intensity monotonic increasing transform. The normalized
average Gabor filter responses magnitudes are more robust than
that of plain Gabor average response-based features, i.e., TGF
and CGF.
For more challenging databases such as Meastex and CUReT,
it is observed that the proposed method is less sensitive to histogram equalization. As shown in Tables V and VI, the classification rates of the combined features drop less than 4% after
performing histogram equalization on the textures. It is also observed that LBP and ACGMRF are robust to histogram equalization, as compared with their performance under the original
texture environment. However, we should bear in mind that the
classification accuracy of the proposed method is much higher
than those of both LBP and ACGMRF.
4) Robustness to Noise: In this section, we evaluate the performance of various methods under the noisy environment. In
the experiment subset #5, the original texture images are added
with additive Gaussian noise with different signal-to-noise ratios (SNR). The classification process is performed 6 times independently in the additive Gaussian noise environment. Then
the average classification accuracies and the standard deviations
are calculated for each database.
As we can see, all approaches maintain their performances
. It is reflected by the high
in all databases when
average classification accuracies and small standard deviation.
The reason is that in such cases, the noise power is negligible
compared to the texture image signal power. The textures are
not significantly distorted. However, when the SNR value drops
below 30, the noise power rises so that different methods have
considerable declines of accuracies.
In the Brodatz database, by combining the DLBP and Gabor
features, it is more robust against noise as they capture both
the local and global texture information. Even though in the
, the proposed method effectively
toughest situation
extract the existing texture information to deliver promising
classification rates. The proposed method achieves moderate
classification accuracy (83.84%) in the toughest case as shown
in Table VII. In the Meastex database, as a challenging database, the classification abilities of various methods reduce
significantly when SNR falls below 15 (Table VIII). In contrast,
the proposed method outperforms other approaches with a
barely moderate accuracy. On the other hand, for the CUReT
database, it contains the largest number of classes which causes
higher risk of mis-classification than other databases mentioned
1117
TABLE VIII
PERFORMANCE OF DIFFERENT FEATURES OF 64 64 IMAGE RESOLUTION IN THE Meastex Database IN THE ADDITIVE GAUSSIAN NOISE ENVIRONMENT OF
2
TABLE IX
PERFORMANCE OF DIFFERENT FEATURES OF 64 64 IMAGE RESOLUTION IN THE CUReT Database IN THE ADDITIVE GAUSSIAN NOISE ENVIRONMENT OF
2
above. High mis-classification risk limits the classification
performances of different techniques. The proposed method
outperforms other methods in the noisy cases (Table IX). Meanwhile, it is able to achieve mediocre classification accuracies
and
, respectively, see
(66.84% and 71.28% for
Table IX) when the SNR is 5.
V. DISCUSSIONS AND CONCLUSIONS
This paper proposes the dominant local binary patterns
(DLBP) as a texture classification approach. The DLBP approach on one side guarantees to be able to represent the
dominant patterns in the texture images. On the other side,
it retains the rotation invariant and histogram equalization
invariant properties of the conventional LBP approach. It is
simple and computationally efficient. Regarding the selection
of the first 80% most frequently appeared patterns as features
after the pattern frequency histogram is constructed and sorted,
as demonstrated in Section II. It is experimentally shown that it
gives good texture classification results.
The global features extracted by using the circularly symmetric Gabor filter responses encapsulate the spatial relationships between distant pixels. The global features extracted from
Gabor filter responses are rotation invariant and less sensitive to
histogram equalization. They, therefore, complement with the
DLBP local features as mirrored by the experimental results.
As demonstrated by the experimental results, it is found that
, the DLBP and the Gabor-based features achieve
when
the best classification results. The robustness of the proposed
approach to image histogram equalization, random rotation and
noise is validated by the experiments carried out under five different image conditions: original textures; histogram equalized
textures; randomly rotated textures and; histogram equalized
and randomly rotated textures; textures with additive Gaussian
noise.
Moreover, its excellent classification performance is demonstrated in three databases, which contain low image resolution
textures (Brodatz), similar appearance textures (Meastex), texture set with large number of classes (CUReT). Besides, the performance of the proposed method is compared with six widely
used image features. The experimental results show that the proposed method outperforms the other image features in terms of
the classification rates in various image conditions.
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S. Liao received the B.Eng. degree (first class honor
and academic award medal) in computer engineering
and the M.Phil. degree in computer science from The
Hong Kong University of Science and Technology in
2005 and 2007, respectively, where he is currently
pursuing the Ph.D. degree in computer science and
engineering.
His research interests include medical image registration, texture analysis, and face recognition. In particular, he has worked on new feature extraction and
image representation methods.
Max W. K. Law received the B.Eng. degree in
computer engineering and the M.Phil. degree in
computer science in 2004 and 2006, respectively,
from The Hong Kong University of Science and
Technology. He is currently pursuing the Ph.D.
degree in computer science and engineering at The
Hong Kong University of Science and Technology.
His research interests include medical image
processing and analysis, vascular segmentation,
medical image registration, edge detection, and
texture recognition.
Albert C. S. Chung received the B.Eng. degree (first
class Honors) in computer engineering from The University of Hong Kong in 1995 and the M.Phil. degree
in computer science from The Hong Kong University
of Science and Technology in 1998.
He joined the Medical Vision Laboratory, University of Oxford, Oxford, U.K., as a doctoral research
student with a Croucher Foundation scholarship and
graduated in 2001. He was a Visiting Scientist at the
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, in 2001. He is currently an Associate Professor with the Department of Computer Science and
Engineering, The Hong Kong University of Science and Technology. His research interests include medical image analysis, image processing, and computer vision.
Dr. Chung won the 2002 British Machine Vision Association Sullivan Thesis
Award for the best doctoral thesis submitted to a U.K. university in the field of
computer or natural vision.

Dominant Local Binary Patterns for Texture Classification

Transcription

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